devxpy
/
glid-3-xl-stable
Stable diffusion, but with more powerful in-painting & out-painting capabilities
Prediction
devxpy/glid-3-xl-stable:760f4043f4f53f4bb5e9b3d1f9268030ae514638e77732916344f1dd4b305607IDthh6qyqtmjbd5adxy7w5s3pfhyStatusSucceededSourceWebHardware–Total durationCreatedInput
- mask
- prompt
- Jon snow from the game of thrones
- edit_image
- num_outputs
- 1
- num_inference_steps
- "500"
{ "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png", "prompt": "Jon snow from the game of thrones", "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "num_inference_steps": "500" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "devxpy/glid-3-xl-stable:760f4043f4f53f4bb5e9b3d1f9268030ae514638e77732916344f1dd4b305607", { input: { mask: "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png", prompt: "Jon snow from the game of thrones", edit_image: "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png", num_outputs: 1, num_inference_steps: "500" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "devxpy/glid-3-xl-stable:760f4043f4f53f4bb5e9b3d1f9268030ae514638e77732916344f1dd4b305607", input={ "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png", "prompt": "Jon snow from the game of thrones", "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "num_inference_steps": "500" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "760f4043f4f53f4bb5e9b3d1f9268030ae514638e77732916344f1dd4b305607", "input": { "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png", "prompt": "Jon snow from the game of thrones", "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "num_inference_steps": "500" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-10-10T19:35:56.409213Z", "created_at": "2022-10-10T19:34:28.966972Z", "data_removed": false, "error": null, "id": "thh6qyqtmjbd5adxy7w5s3pfhy", "input": { "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png", "prompt": "Jon snow from the game of thrones", "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "num_inference_steps": "500" }, "logs": "$ /root/.pyenv/versions/3.10.7/bin/python sample.py --model_path inpaint.pt --edit /tmp/tmpc_o91y0t205032562_345087143808969_4674423480430035886_n.png --mask /tmp/tmpvkqeu1cpface_mask.png --steps 500 --text Jon snow from the game of thrones --num_batches 1\nUsing device: cuda:0\nmaking attention of type 'vanilla' with 512 in_channels\nWorking with z of shape (1, 4, 32, 32) = 4096 dimensions.\nmaking attention of type 'vanilla' with 512 in_channels\nSome weights of the model 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'vision_model.encoder.layers.1.mlp.fc2.weight', 'vision_model.post_layernorm.bias', 'vision_model.encoder.layers.3.self_attn.out_proj.weight', 'vision_model.embeddings.class_embedding', 'vision_model.encoder.layers.5.self_attn.v_proj.bias', 'vision_model.encoder.layers.18.layer_norm2.weight', 'vision_model.encoder.layers.0.layer_norm1.bias', 'vision_model.encoder.layers.1.self_attn.v_proj.bias', 'vision_model.encoder.layers.22.self_attn.v_proj.bias', 'vision_model.encoder.layers.23.self_attn.v_proj.weight', 'vision_model.encoder.layers.12.mlp.fc1.weight', 'vision_model.encoder.layers.19.mlp.fc1.bias', 'vision_model.encoder.layers.22.self_attn.out_proj.weight', 'vision_model.encoder.layers.5.self_attn.k_proj.weight', 'vision_model.encoder.layers.12.self_attn.v_proj.weight', 'vision_model.encoder.layers.1.layer_norm1.weight', 'vision_model.encoder.layers.4.self_attn.q_proj.weight', 'vision_model.encoder.layers.12.self_attn.q_proj.weight', 'vision_model.encoder.layers.9.layer_norm1.weight', 'vision_model.encoder.layers.15.mlp.fc2.bias', 'vision_model.encoder.layers.16.mlp.fc2.bias', 'vision_model.encoder.layers.14.mlp.fc2.bias', 'vision_model.encoder.layers.19.self_attn.v_proj.bias', 'vision_model.encoder.layers.2.self_attn.q_proj.bias', 'vision_model.encoder.layers.5.self_attn.k_proj.bias', 'vision_model.encoder.layers.12.self_attn.k_proj.weight', 'vision_model.encoder.layers.19.self_attn.out_proj.weight', 'vision_model.encoder.layers.19.self_attn.out_proj.bias', 'vision_model.encoder.layers.4.layer_norm2.weight', 'vision_model.encoder.layers.14.mlp.fc2.weight', 'vision_model.encoder.layers.12.mlp.fc2.bias', 'vision_model.post_layernorm.weight', 'vision_model.encoder.layers.0.mlp.fc2.weight', 'vision_model.encoder.layers.14.self_attn.k_proj.bias', 'vision_model.encoder.layers.1.mlp.fc1.weight', 'vision_model.encoder.layers.22.layer_norm1.weight', 'vision_model.encoder.layers.17.mlp.fc1.weight', 'vision_model.encoder.layers.9.mlp.fc1.weight', 'logit_scale', 'vision_model.encoder.layers.12.layer_norm2.weight', 'vision_model.encoder.layers.9.self_attn.out_proj.weight', 'vision_model.encoder.layers.11.layer_norm1.bias', 'vision_model.encoder.layers.16.mlp.fc1.weight', 'vision_model.encoder.layers.18.self_attn.q_proj.bias', 'vision_model.encoder.layers.23.layer_norm2.bias', 'vision_model.encoder.layers.17.self_attn.k_proj.bias', 'vision_model.encoder.layers.18.mlp.fc1.weight']\n- This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n- This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n/src/sample.py:354: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). 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[01:01<00:00, 8.83it/s]\n 99%|█████████▉| 497/500 [01:02<00:00, 8.84it/s]\n100%|█████████▉| 498/500 [01:02<00:00, 8.84it/s]\n100%|█████████▉| 499/500 [01:02<00:00, 8.84it/s]\n100%|██████████| 500/500 [01:02<00:00, 8.84it/s]\n100%|██████████| 500/500 [01:02<00:00, 8.02it/s]", "metrics": { "predict_time": 87.166102, "total_time": 87.442241 }, "output": [ "https://replicate.delivery/mgxm/9143aab6-0adf-4c5b-a58c-e38166a48c58/00000.png" ], "started_at": "2022-10-10T19:34:29.243111Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/thh6qyqtmjbd5adxy7w5s3pfhy", "cancel": "https://api.replicate.com/v1/predictions/thh6qyqtmjbd5adxy7w5s3pfhy/cancel" }, "version": "760f4043f4f53f4bb5e9b3d1f9268030ae514638e77732916344f1dd4b305607" }Generated in$ /root/.pyenv/versions/3.10.7/bin/python sample.py --model_path inpaint.pt --edit /tmp/tmpc_o91y0t205032562_345087143808969_4674423480430035886_n.png --mask /tmp/tmpvkqeu1cpface_mask.png --steps 500 --text Jon snow from the game of thrones --num_batches 1 Using device: cuda:0 making attention of type 'vanilla' with 512 in_channels Working with z of shape (1, 4, 32, 32) = 4096 dimensions. making attention of type 'vanilla' with 512 in_channels Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.19.self_attn.k_proj.bias', 'vision_model.encoder.layers.21.layer_norm1.weight', 'vision_model.encoder.layers.0.self_attn.out_proj.bias', 'vision_model.encoder.layers.14.layer_norm1.bias', 'vision_model.encoder.layers.21.mlp.fc2.weight', 'vision_model.encoder.layers.3.mlp.fc1.bias', 'vision_model.encoder.layers.11.mlp.fc1.weight', 'vision_model.encoder.layers.3.self_attn.v_proj.bias', 'vision_model.encoder.layers.10.mlp.fc2.weight', 'vision_model.encoder.layers.8.self_attn.q_proj.bias', 'vision_model.encoder.layers.6.layer_norm1.bias', 'vision_model.encoder.layers.18.self_attn.k_proj.bias', 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'vision_model.encoder.layers.12.mlp.fc2.bias', 'vision_model.post_layernorm.weight', 'vision_model.encoder.layers.0.mlp.fc2.weight', 'vision_model.encoder.layers.14.self_attn.k_proj.bias', 'vision_model.encoder.layers.1.mlp.fc1.weight', 'vision_model.encoder.layers.22.layer_norm1.weight', 'vision_model.encoder.layers.17.mlp.fc1.weight', 'vision_model.encoder.layers.9.mlp.fc1.weight', 'logit_scale', 'vision_model.encoder.layers.12.layer_norm2.weight', 'vision_model.encoder.layers.9.self_attn.out_proj.weight', 'vision_model.encoder.layers.11.layer_norm1.bias', 'vision_model.encoder.layers.16.mlp.fc1.weight', 'vision_model.encoder.layers.18.self_attn.q_proj.bias', 'vision_model.encoder.layers.23.layer_norm2.bias', 'vision_model.encoder.layers.17.self_attn.k_proj.bias', 'vision_model.encoder.layers.18.mlp.fc1.weight'] - This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). - This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). /src/sample.py:354: DeprecationWarning: ANTIALIAS is deprecated and will be removed in Pillow 10 (2023-07-01). Use Resampling.LANCZOS instead. mask_image = mask_image.resize((input_image.shape[3],input_image.shape[2]), Image.ANTIALIAS) 0%| | 0/500 [00:00<?, ?it/s] 0%| | 1/500 [00:00<05:47, 1.44it/s] 0%| | 2/500 [00:01<04:35, 1.80it/s] 1%| | 3/500 [00:01<04:13, 1.96it/s] 1%| | 4/500 [00:01<02:55, 2.83it/s] 1%| | 5/500 [00:01<02:12, 3.74it/s] 1%| | 6/500 [00:01<01:46, 4.65it/s] 1%|▏ | 7/500 [00:02<01:29, 5.49it/s] 2%|▏ | 8/500 [00:02<01:19, 6.23it/s] 2%|▏ | 9/500 [00:02<01:11, 6.83it/s] 2%|▏ | 10/500 [00:02<01:06, 7.34it/s] 2%|▏ | 11/500 [00:02<01:03, 7.73it/s] 2%|▏ | 12/500 [00:02<01:00, 8.03it/s] 3%|▎ | 13/500 [00:02<00:59, 8.24it/s] 3%|▎ | 14/500 [00:02<00:57, 8.40it/s] 3%|▎ | 15/500 [00:02<00:56, 8.51it/s] 3%|▎ | 16/500 [00:03<00:56, 8.59it/s] 3%|▎ | 17/500 [00:03<00:55, 8.65it/s] 4%|▎ | 18/500 [00:03<00:55, 8.69it/s] 4%|▍ | 19/500 [00:03<00:55, 8.69it/s] 4%|▍ | 20/500 [00:03<00:55, 8.70it/s] 4%|▍ | 21/500 [00:03<00:54, 8.73it/s] 4%|▍ | 22/500 [00:03<00:54, 8.75it/s] 5%|▍ | 23/500 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devxpy/glid-3-xl-stable:773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98Input
- fp32
- prompt
- Jon snow from the game of thrones
- init_image
- num_outputs
- 1
- skip_timesteps
- 50
- num_inference_steps
- "100"
{ "fp32": true, "prompt": "Jon snow from the game of thrones", "init_image": "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "skip_timesteps": 50, "num_inference_steps": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "devxpy/glid-3-xl-stable:773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98", { input: { fp32: true, prompt: "Jon snow from the game of thrones", init_image: "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png", num_outputs: 1, skip_timesteps: 50, num_inference_steps: "100" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "devxpy/glid-3-xl-stable:773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98", input={ "fp32": True, "prompt": "Jon snow from the game of thrones", "init_image": "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "skip_timesteps": 50, "num_inference_steps": "100" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98", "input": { "fp32": true, "prompt": "Jon snow from the game of thrones", "init_image": "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "skip_timesteps": 50, "num_inference_steps": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-10-12T19:13:28.053574Z", "created_at": "2022-10-12T19:13:19.441602Z", "data_removed": false, "error": null, "id": "ymcrh3qlzrepri4btsdtlufjua", "input": { "fp32": true, "prompt": "Jon snow from the game of thrones", "init_image": "https://replicate.delivery/mgxm/a5f6ab5d-713a-441e-ac98-fac49c74c3b3/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "skip_timesteps": 50, "num_inference_steps": "100" }, "logs": "Using device: cuda:0\n\n 0%| | 0/50 [00:00<?, ?it/s]\n 2%|▏ | 1/50 [00:00<00:33, 1.47it/s]\n 4%|▍ | 2/50 [00:01<00:26, 1.83it/s]\n 6%|▌ | 3/50 [00:01<00:23, 1.99it/s]\n 8%|▊ | 4/50 [00:01<00:16, 2.87it/s]\n 10%|█ | 5/50 [00:01<00:11, 3.79it/s]\n 12%|█▏ | 6/50 [00:01<00:09, 4.71it/s]\n 14%|█▍ | 7/50 [00:02<00:07, 5.56it/s]\n 16%|█▌ | 8/50 [00:02<00:06, 6.30it/s]\n 18%|█▊ | 9/50 [00:02<00:05, 6.92it/s]\n 20%|██ | 10/50 [00:02<00:05, 7.42it/s]\n 22%|██▏ | 11/50 [00:02<00:05, 7.80it/s]\n 24%|██▍ | 12/50 [00:02<00:04, 8.08it/s]\n 26%|██▌ | 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8.79it/s]\n 72%|███████▏ | 36/50 [00:05<00:01, 8.80it/s]\n 74%|███████▍ | 37/50 [00:05<00:01, 8.80it/s]\n 76%|███████▌ | 38/50 [00:05<00:01, 8.80it/s]\n 78%|███████▊ | 39/50 [00:05<00:01, 8.80it/s]\n 80%|████████ | 40/50 [00:05<00:01, 8.78it/s]\n 82%|████████▏ | 41/50 [00:06<00:01, 5.91it/s]\n 84%|████████▍ | 42/50 [00:06<00:01, 6.55it/s]\n 86%|████████▌ | 43/50 [00:06<00:00, 7.10it/s]\n 88%|████████▊ | 44/50 [00:06<00:00, 7.55it/s]\n 90%|█████████ | 45/50 [00:06<00:00, 7.89it/s]\n 92%|█████████▏| 46/50 [00:06<00:00, 8.15it/s]\n 94%|█████████▍| 47/50 [00:06<00:00, 8.34it/s]\n 96%|█████████▌| 48/50 [00:07<00:00, 8.48it/s]\n 98%|█████████▊| 49/50 [00:07<00:00, 8.57it/s]\n100%|██████████| 50/50 [00:07<00:00, 8.65it/s]\n100%|██████████| 50/50 [00:07<00:00, 6.85it/s]", "metrics": { "predict_time": 8.385183, "total_time": 8.611972 }, "output": [ "https://replicate.delivery/mgxm/c4264c25-682b-4f33-8625-3f14f546e1e0/00000.png" ], "started_at": "2022-10-12T19:13:19.668391Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/ymcrh3qlzrepri4btsdtlufjua", "cancel": "https://api.replicate.com/v1/predictions/ymcrh3qlzrepri4btsdtlufjua/cancel" }, "version": "773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98" }Generated inUsing device: cuda:0 0%| | 0/50 [00:00<?, ?it/s] 2%|▏ | 1/50 [00:00<00:33, 1.47it/s] 4%|▍ | 2/50 [00:01<00:26, 1.83it/s] 6%|▌ | 3/50 [00:01<00:23, 1.99it/s] 8%|▊ | 4/50 [00:01<00:16, 2.87it/s] 10%|█ | 5/50 [00:01<00:11, 3.79it/s] 12%|█▏ | 6/50 [00:01<00:09, 4.71it/s] 14%|█▍ | 7/50 [00:02<00:07, 5.56it/s] 16%|█▌ | 8/50 [00:02<00:06, 6.30it/s] 18%|█▊ | 9/50 [00:02<00:05, 6.92it/s] 20%|██ | 10/50 [00:02<00:05, 7.42it/s] 22%|██▏ | 11/50 [00:02<00:05, 7.80it/s] 24%|██▍ | 12/50 [00:02<00:04, 8.08it/s] 26%|██▌ | 13/50 [00:02<00:04, 8.30it/s] 28%|██▊ | 14/50 [00:02<00:04, 8.45it/s] 30%|███ | 15/50 [00:02<00:04, 8.56it/s] 32%|███▏ | 16/50 [00:03<00:03, 8.65it/s] 34%|███▍ | 17/50 [00:03<00:03, 8.70it/s] 36%|███▌ | 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devxpy/glid-3-xl-stable:773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98IDefgpyuzn6ravdij3yl42rxkk2aStatusSucceededSourceWebHardware–Total durationCreatedInput
- fp32
- mask
- prompt
- eiffel tower in paris
- outpaint
- wider
- edit_image
- num_outputs
- 1
- num_inference_steps
- "200"
{ "fp32": true, "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png", "prompt": "eiffel tower in paris", "outpaint": "wider", "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "num_inference_steps": "200" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "devxpy/glid-3-xl-stable:773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98", { input: { fp32: true, mask: "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png", prompt: "eiffel tower in paris", outpaint: "wider", edit_image: "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png", num_outputs: 1, num_inference_steps: "200" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "devxpy/glid-3-xl-stable:773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98", input={ "fp32": True, "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png", "prompt": "eiffel tower in paris", "outpaint": "wider", "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "num_inference_steps": "200" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98", "input": { "fp32": true, "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png", "prompt": "eiffel tower in paris", "outpaint": "wider", "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "num_inference_steps": "200" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-10-12T19:45:06.507696Z", "created_at": "2022-10-12T19:43:09.581398Z", "data_removed": false, "error": null, "id": "efgpyuzn6ravdij3yl42rxkk2a", "input": { "fp32": true, "mask": "https://replicate.delivery/mgxm/e2dfe2c5-7578-421e-ab55-ae9b553243ec/face_mask.png", "prompt": "eiffel tower in paris", "outpaint": "wider", "edit_image": "https://replicate.delivery/mgxm/8a83b0ca-2ea7-4095-8057-499f35a085d8/205032562_345087143808969_4674423480430035886_n.png", "num_outputs": 1, "num_inference_steps": "200" }, "logs": "Using device: cuda:0\n\n 0%| | 0/200 [00:00<?, ?it/s]\n 0%| | 1/200 [00:00<02:25, 1.36it/s]\n 1%| | 2/200 [00:01<01:52, 1.76it/s]\n 2%|▏ | 3/200 [00:01<01:41, 1.94it/s]\n 2%|▏ | 4/200 [00:01<01:09, 2.81it/s]\n 2%|▎ | 5/200 [00:01<00:52, 3.72it/s]\n 3%|▎ | 6/200 [00:01<00:41, 4.63it/s]\n 4%|▎ | 7/200 [00:02<00:35, 5.48it/s]\n 4%|▍ | 8/200 [00:02<00:30, 6.23it/s]\n 4%|▍ | 9/200 [00:02<00:27, 6.86it/s]\n 5%|▌ | 10/200 [00:02<00:25, 7.37it/s]\n 6%|▌ | 11/200 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[00:25<00:00, 8.83it/s]\n 98%|█████████▊| 195/200 [00:25<00:00, 8.83it/s]\n 98%|█████████▊| 196/200 [00:25<00:00, 8.83it/s]\n 98%|█████████▊| 197/200 [00:25<00:00, 8.83it/s]\n 99%|█████████▉| 198/200 [00:26<00:00, 8.84it/s]\n100%|█████████▉| 199/200 [00:26<00:00, 8.84it/s]\n100%|██████████| 200/200 [00:26<00:00, 8.84it/s]\n100%|██████████| 200/200 [00:26<00:00, 7.60it/s]", "metrics": { "predict_time": 79.998951, "total_time": 116.926298 }, "output": [ "https://replicate.delivery/mgxm/75523733-bf38-4f0c-bd51-60e9aa87a9df/00000.png" ], "started_at": "2022-10-12T19:43:46.508745Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/efgpyuzn6ravdij3yl42rxkk2a", "cancel": "https://api.replicate.com/v1/predictions/efgpyuzn6ravdij3yl42rxkk2a/cancel" }, "version": "773fe7b990c57d29570b2d6fc71a0f460b129c8c950b5e1b1b9c881581387c98" }Generated inUsing device: cuda:0 0%| | 0/200 [00:00<?, ?it/s] 0%| | 1/200 [00:00<02:25, 1.36it/s] 1%| | 2/200 [00:01<01:52, 1.76it/s] 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[00:25<00:00, 8.81it/s] 96%|█████████▋| 193/200 [00:25<00:00, 8.82it/s] 97%|█████████▋| 194/200 [00:25<00:00, 8.83it/s] 98%|█████████▊| 195/200 [00:25<00:00, 8.83it/s] 98%|█████████▊| 196/200 [00:25<00:00, 8.83it/s] 98%|█████████▊| 197/200 [00:25<00:00, 8.83it/s] 99%|█████████▉| 198/200 [00:26<00:00, 8.84it/s] 100%|█████████▉| 199/200 [00:26<00:00, 8.84it/s] 100%|██████████| 200/200 [00:26<00:00, 8.84it/s] 100%|██████████| 200/200 [00:26<00:00, 7.60it/s]Prediction
devxpy/glid-3-xl-stable:d53d0cf59b46f622265ad5924be1e536d6a371e8b1eaceeebc870b6001a0659bIDp5icamcxpng5ln4bmo5cfot53eStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- mask
- prompt
- scarlett johansson wearing earing
- edit_image
- num_outputs
- 1
- num_inference_steps
- "100"
{ "mask": "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png", "prompt": "scarlett johansson wearing earing", "edit_image": "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png", "num_outputs": 1, "num_inference_steps": "100" }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "devxpy/glid-3-xl-stable:d53d0cf59b46f622265ad5924be1e536d6a371e8b1eaceeebc870b6001a0659b", { input: { mask: "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png", prompt: "scarlett johansson wearing earing", edit_image: "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png", num_outputs: 1, num_inference_steps: "100" } } ); // To access the file URL: console.log(output[0].url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "devxpy/glid-3-xl-stable:d53d0cf59b46f622265ad5924be1e536d6a371e8b1eaceeebc870b6001a0659b", input={ "mask": "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png", "prompt": "scarlett johansson wearing earing", "edit_image": "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png", "num_outputs": 1, "num_inference_steps": "100" } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run devxpy/glid-3-xl-stable using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "d53d0cf59b46f622265ad5924be1e536d6a371e8b1eaceeebc870b6001a0659b", "input": { "mask": "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png", "prompt": "scarlett johansson wearing earing", "edit_image": "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png", "num_outputs": 1, "num_inference_steps": "100" } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2022-10-27T00:34:59.688826Z", "created_at": "2022-10-27T00:34:45.923001Z", "data_removed": false, "error": null, "id": "p5icamcxpng5ln4bmo5cfot53e", "input": { "mask": "https://replicate.delivery/mgxm/9e666bd2-3d28-4346-b73c-c83cef29f758/mask.png", "prompt": "scarlett johansson wearing earing", "edit_image": "https://replicate.delivery/mgxm/9a79954b-ce76-4f83-8189-b4d3b28e3543/orig.png", "num_outputs": 1, "num_inference_steps": "100" }, "logs": "Using device: cuda:0\n\n 0%| | 0/100 [00:00<?, ?it/s]\n 1%| | 1/100 [00:00<01:00, 1.64it/s]\n 2%|▏ | 2/100 [00:01<00:49, 1.97it/s]\n 3%|▎ | 3/100 [00:01<00:45, 2.11it/s]\n 4%|▍ | 4/100 [00:01<00:31, 3.03it/s]\n 5%|▌ | 5/100 [00:01<00:23, 4.00it/s]\n 6%|▌ | 6/100 [00:01<00:19, 4.95it/s]\n 7%|▋ | 7/100 [00:01<00:15, 5.82it/s]\n 8%|▊ | 8/100 [00:02<00:13, 6.59it/s]\n 9%|▉ | 9/100 [00:02<00:12, 7.23it/s]\n 10%|█ | 10/100 [00:02<00:11, 7.73it/s]\n 11%|█ | 11/100 [00:02<00:10, 8.12it/s]\n 12%|█▏ | 12/100 [00:02<00:10, 8.40it/s]\n 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[00:10<00:02, 7.89it/s]\n 80%|████████ | 80/100 [00:10<00:02, 8.23it/s]\n 81%|████████ | 81/100 [00:10<00:02, 8.49it/s]\n 82%|████████▏ | 82/100 [00:10<00:02, 8.67it/s]\n 83%|████████▎ | 83/100 [00:10<00:01, 8.81it/s]\n 84%|████████▍ | 84/100 [00:10<00:01, 8.91it/s]\n 85%|████████▌ | 85/100 [00:10<00:01, 8.98it/s]\n 86%|████████▌ | 86/100 [00:11<00:01, 9.03it/s]\n 87%|████████▋ | 87/100 [00:11<00:01, 9.06it/s]\n 88%|████████▊ | 88/100 [00:11<00:01, 9.08it/s]\n 89%|████████▉ | 89/100 [00:11<00:01, 9.10it/s]\n 90%|█████████ | 90/100 [00:11<00:01, 9.09it/s]\n 91%|█████████ | 91/100 [00:11<00:00, 9.10it/s]\n 92%|█████████▏| 92/100 [00:11<00:00, 9.11it/s]\n 93%|█████████▎| 93/100 [00:11<00:00, 9.12it/s]\n 94%|█████████▍| 94/100 [00:11<00:00, 9.14it/s]\n 95%|█████████▌| 95/100 [00:12<00:00, 9.14it/s]\n 96%|█████████▌| 96/100 [00:12<00:00, 9.14it/s]\n 97%|█████████▋| 97/100 [00:12<00:00, 9.14it/s]\n 98%|█████████▊| 98/100 [00:12<00:00, 9.15it/s]\n 99%|█████████▉| 99/100 [00:12<00:00, 9.15it/s]\n100%|██████████| 100/100 [00:12<00:00, 9.16it/s]\n100%|██████████| 100/100 [00:12<00:00, 7.95it/s]", "metrics": { "predict_time": 13.793986, "total_time": 13.765825 }, "output": [ "https://replicate.delivery/pbxt/VwdejnwBR21fVUrXdjRECDn6etIQLiW0QdvFlTtUCdXmhwzfA/00000.png" ], "started_at": "2022-10-27T00:34:45.894840Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/p5icamcxpng5ln4bmo5cfot53e", "cancel": "https://api.replicate.com/v1/predictions/p5icamcxpng5ln4bmo5cfot53e/cancel" }, "version": "d53d0cf59b46f622265ad5924be1e536d6a371e8b1eaceeebc870b6001a0659b" }Generated inUsing device: cuda:0 0%| | 0/100 [00:00<?, ?it/s] 1%| | 1/100 [00:00<01:00, 1.64it/s] 2%|▏ | 2/100 [00:01<00:49, 1.97it/s] 3%|▎ | 3/100 [00:01<00:45, 2.11it/s] 4%|▍ | 4/100 [00:01<00:31, 3.03it/s] 5%|▌ | 5/100 [00:01<00:23, 4.00it/s] 6%|▌ | 6/100 [00:01<00:19, 4.95it/s] 7%|▋ | 7/100 [00:01<00:15, 5.82it/s] 8%|▊ | 8/100 [00:02<00:13, 6.59it/s] 9%|▉ | 9/100 [00:02<00:12, 7.23it/s] 10%|█ | 10/100 [00:02<00:11, 7.73it/s] 11%|█ | 11/100 [00:02<00:10, 8.12it/s] 12%|█▏ | 12/100 [00:02<00:10, 8.40it/s] 13%|█▎ | 13/100 [00:02<00:10, 8.62it/s] 14%|█▍ | 14/100 [00:02<00:09, 8.78it/s] 15%|█▌ | 15/100 [00:02<00:09, 8.89it/s] 16%|█▌ | 16/100 [00:02<00:09, 8.98it/s] 17%|█▋ | 17/100 [00:03<00:09, 9.03it/s] 18%|█▊ | 18/100 [00:03<00:09, 9.07it/s] 19%|█▉ | 19/100 [00:03<00:08, 9.11it/s] 20%|██ | 20/100 [00:03<00:08, 9.12it/s] 21%|██ | 21/100 [00:03<00:08, 9.14it/s] 22%|██▏ | 22/100 [00:03<00:08, 9.15it/s] 23%|██▎ | 23/100 [00:03<00:08, 9.15it/s] 24%|██▍ | 24/100 [00:03<00:08, 9.16it/s] 25%|██▌ | 25/100 [00:03<00:08, 9.16it/s] 26%|██▌ | 26/100 [00:04<00:11, 6.22it/s] 27%|██▋ | 27/100 [00:04<00:10, 6.87it/s] 28%|██▊ | 28/100 [00:04<00:09, 7.43it/s] 29%|██▉ | 29/100 [00:04<00:09, 7.88it/s] 30%|███ | 30/100 [00:04<00:08, 8.22it/s] 31%|███ | 31/100 [00:04<00:08, 8.49it/s] 32%|███▏ | 32/100 [00:04<00:07, 8.68it/s] 33%|███▎ | 33/100 [00:04<00:07, 8.83it/s] 34%|███▍ | 34/100 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9.16it/s] 100%|██████████| 100/100 [00:12<00:00, 7.95it/s]
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